Created On: Mar 24, 2017 | Last Updated: Jan 27, 2025 | Last Verified: Nov 05, 2024
Author: Sasank Chilamkurthy
In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. You can read more about the transfer learning at cs231n notes
Quoting these notes,
In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.
These two major transfer learning scenarios look as follows:
Finetuning the ConvNet: Instead of random initialization, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. Rest of the training looks as usual.
ConvNet as fixed feature extractor: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.
# License: BSD # Author: Sasank Chilamkurthy import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler import torch.backends.cudnn as cudnn import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os from PIL import Image from tempfile import TemporaryDirectory cudnn.benchmark = True plt.ion() # interactive mode
<contextlib.ExitStack object at 0x7f4c68c0c7c0>Load Data#
We will use torchvision and torch.utils.data packages for loading the data.
The problem we’re going to solve today is to train a model to classify ants and bees. We have about 120 training images each for ants and bees. There are 75 validation images for each class. Usually, this is a very small dataset to generalize upon, if trained from scratch. Since we are using transfer learning, we should be able to generalize reasonably well.
This dataset is a very small subset of imagenet.
Note
Download the data from here and extract it to the current directory.
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.
device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")
Visualize a few images#
Let’s visualize a few training images so as to understand the data augmentations.
def imshow(inp, title=None):
"""Display image for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))
# Make a grid from batch
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
Training the model#
Now, let’s write a general function to train a model. Here, we will illustrate:
Scheduling the learning rate
Saving the best model
In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler.
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
# Create a temporary directory to save training checkpoints
with TemporaryDirectory() as tempdir:
best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')
torch.save(model.state_dict(), best_model_params_path)
best_acc = 0.0
for epoch in range(num_epochs):
print(f'Epoch {epoch}/{num_epochs - 1}')
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
torch.save(model.state_dict(), best_model_params_path)
print()
time_elapsed = time.time() - since
print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Best val Acc: {best_acc:4f}')
# load best model weights
model.load_state_dict(torch.load(best_model_params_path, weights_only=True))
return model
Visualizing the model predictions#
Generic function to display predictions for a few images
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title(f'predicted: {class_names[preds[j]]}')
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
Finetuning the ConvNet#
Load a pretrained model and reset final fully connected layer.
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth 0%| | 0.00/44.7M [00:00<?, ?B/s] 94%|█████████▍| 42.1M/44.7M [00:00<00:00, 441MB/s] 100%|██████████| 44.7M/44.7M [00:00<00:00, 440MB/s]Train and evaluate#
It should take around 15-25 min on CPU. On GPU though, it takes less than a minute.
Epoch 0/24 ---------- train Loss: 0.5745 Acc: 0.6926 val Loss: 0.1612 Acc: 0.9346 Epoch 1/24 ---------- train Loss: 0.5432 Acc: 0.7623 val Loss: 0.3591 Acc: 0.8627 Epoch 2/24 ---------- train Loss: 0.5119 Acc: 0.7336 val Loss: 0.3765 Acc: 0.8758 Epoch 3/24 ---------- train Loss: 0.4419 Acc: 0.8320 val Loss: 0.4508 Acc: 0.8366 Epoch 4/24 ---------- train Loss: 0.4929 Acc: 0.8115 val Loss: 0.1988 Acc: 0.9085 Epoch 5/24 ---------- train Loss: 0.4073 Acc: 0.8238 val Loss: 0.2791 Acc: 0.8954 Epoch 6/24 ---------- train Loss: 0.4813 Acc: 0.8156 val Loss: 0.2940 Acc: 0.8889 Epoch 7/24 ---------- train Loss: 0.4508 Acc: 0.8402 val Loss: 0.2450 Acc: 0.9216 Epoch 8/24 ---------- train Loss: 0.3920 Acc: 0.8320 val Loss: 0.2712 Acc: 0.9150 Epoch 9/24 ---------- train Loss: 0.3082 Acc: 0.8770 val Loss: 0.2294 Acc: 0.9150 Epoch 10/24 ---------- train Loss: 0.2437 Acc: 0.9098 val Loss: 0.2657 Acc: 0.9216 Epoch 11/24 ---------- train Loss: 0.3398 Acc: 0.8484 val Loss: 0.2255 Acc: 0.9216 Epoch 12/24 ---------- train Loss: 0.2634 Acc: 0.8893 val Loss: 0.2037 Acc: 0.9150 Epoch 13/24 ---------- train Loss: 0.1894 Acc: 0.9262 val Loss: 0.2578 Acc: 0.9150 Epoch 14/24 ---------- train Loss: 0.2690 Acc: 0.8566 val Loss: 0.2170 Acc: 0.9216 Epoch 15/24 ---------- train Loss: 0.2729 Acc: 0.8648 val Loss: 0.2010 Acc: 0.9216 Epoch 16/24 ---------- train Loss: 0.3079 Acc: 0.8770 val Loss: 0.2117 Acc: 0.9216 Epoch 17/24 ---------- train Loss: 0.2758 Acc: 0.8975 val Loss: 0.2322 Acc: 0.9150 Epoch 18/24 ---------- train Loss: 0.2639 Acc: 0.8852 val Loss: 0.2398 Acc: 0.9150 Epoch 19/24 ---------- train Loss: 0.3235 Acc: 0.8566 val Loss: 0.2716 Acc: 0.9020 Epoch 20/24 ---------- train Loss: 0.2691 Acc: 0.8852 val Loss: 0.2301 Acc: 0.9216 Epoch 21/24 ---------- train Loss: 0.2086 Acc: 0.9098 val Loss: 0.2055 Acc: 0.9216 Epoch 22/24 ---------- train Loss: 0.3290 Acc: 0.8648 val Loss: 0.2559 Acc: 0.9150 Epoch 23/24 ---------- train Loss: 0.3096 Acc: 0.8566 val Loss: 0.2090 Acc: 0.9216 Epoch 24/24 ---------- train Loss: 0.3332 Acc: 0.8402 val Loss: 0.2022 Acc: 0.9216 Training complete in 0m 34s Best val Acc: 0.934641
visualize_model(model_ft)Inference on custom images#
Use the trained model to make predictions on custom images and visualize the predicted class labels along with the images.
def visualize_model_predictions(model,img_path):
was_training = model.training
model.eval()
img = Image.open(img_path)
img = data_transforms['val'](img)
img = img.unsqueeze(0)
img = img.to(device)
with torch.no_grad():
outputs = model(img)
_, preds = torch.max(outputs, 1)
ax = plt.subplot(2,2,1)
ax.axis('off')
ax.set_title(f'Predicted: {class_names[preds[0]]}')
imshow(img.cpu().data[0])
model.train(mode=was_training)
visualize_model_predictions(
model_conv,
img_path='data/hymenoptera_data/val/bees/72100438_73de9f17af.jpg'
)
plt.ioff()
plt.show()
Further Learning#
If you would like to learn more about the applications of transfer learning, checkout our Quantized Transfer Learning for Computer Vision Tutorial.
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